63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
from datetime import datetime
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from collections import Counter
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import pandas as pd
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from sqlalchemy import select
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from data.database import Base, dal, project_variables
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import util.drawing as drawutil
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import util.environment as envutil
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project_variables = envutil.load_environment_variables()
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def load_dataframe(
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table_name: str, start_date: datetime | None = None, end_date: datetime | None = None
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) -> pd.DataFrame:
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"""connect to the database and load game data into a pandas dataframe"""
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dal.connect()
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session = dal.Session()
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game_dict = drawutil.check_table_name(table_name=table_name)
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if game_dict:
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target_table = Base.metadata.tables.get(game_dict['db_table_name'])
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if start_date is not None and end_date is not None:
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sql_statement = (select(target_table)
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.where(target_table.columns.draw_date >= start_date)
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.where(target_table.columns.draw_date <= end_date))
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elif start_date is not None and end_date is None:
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sql_statement = (select(target_table).where(target_table.columns.draw_date >= start_date))
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elif start_date is None and end_date is not None:
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sql_statement = (select(target_table).where(target_table.columns.draw_date <= end_date))
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else:
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sql_statement = select(target_table)
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return pd.read_sql(sql_statement, session.bind)
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else:
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raise ValueError('An invalid table name was provided')
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def get_most_common_number(
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data_frame: pd.DataFrame,
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columns: list | None = None,
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top: int = 1
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) -> list[int]:
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if columns is None:
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columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
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flat_numbers = data_frame[columns].values.flatten()
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counts = Counter(flat_numbers)
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return [int(num) for num, _ in counts.most_common(top)]
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def get_least_common_number(
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data_frame: pd.DataFrame,
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columns: list | None = None,
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bottom: int = 1
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) -> list[int]:
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if columns is None:
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columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
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flat_numbers = data_frame[columns].values.flatten()
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counts = Counter(flat_numbers)
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return list(reversed([int(num) for num, _ in counts.most_common()[-bottom:]]))
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